Prefabricated house wallboard prefabrication mold optimization method

Abstract

The invention discloses a prefabricated house wallboard prefabricating mold optimization method. The method comprises the following steps that S1, scene description is carried out to determine information of prefabricated wallboard production modules of a prefabricated house; s2, mold matching combination calculation: calculating all matching combinations of the molds through a recursive function; S3, establishing an integer linear programming model; and S4, solving the integer linear programming model. The combination optimization result of the wallboard prefabricating mold is obtained. According to the method, the mold matching combination optimization model is established with the minimum mold table batches as the target, the optimal mold combination production batch scheme can be calculated through a mathematical programming method, efficient exhaustion of mold combination can be achieved through a recursion method, the direction of a wallboard can be adjusted in a self-adaptive mode, the mold table space is further saved, and the production efficiency is improved. And finally, the wallboard prefabrication production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of optimization of prefabricated molds for assembled residential wall panels, and more specifically, to a method for optimizing prefabricated molds for assembled residential wall panels. Background Art

[0002] Prefabricated housing transfers a large amount of on-site work in traditional construction methods to factories, processes and manufactures components in factories, and transports them to the site for assembly. This environmentally friendly method can save costs and improve efficiency. In the prefabrication process of prefabricated housing components, molds need to be arranged on the mold table. The size and quantity of molds vary. Therefore, the combination of molds from different batches will affect the efficiency of the mold table. Unreasonable combination will lead to an increase in mold batches and working hours, resulting in a decrease in production efficiency. Therefore, the optimization of the combination of molds from different batches is very important for the production efficiency of component prefabrication. Summary of the invention

[0003] In order to achieve these purposes and other advantages according to the present invention, a preferred embodiment of the present invention provides a method for optimizing a prefabricated mold for a prefabricated residential wall panel, comprising the following steps:

[0004] S1. Scene Description

[0005] Determine information on the production modules of prefabricated wall panels for prefabricated housing;

[0006] The prefabricated wall panel production module includes a mold platform and a mold. A plurality of the molds are arranged at intervals and are located on the mold platform in a straight line. The length of the mold platform is a and the width is b. All the mold platforms as a whole have a distance between the left side and the edge of the mold of no less than d1, a distance between the right side of the whole mold platform and the edge of the mold of no less than d3, a distance between the upper side and the lower side of the whole mold platform and the mold of no less than d4, and a distance between two adjacent molds of no less than d2.

[0007] Wall panels are all rectangular. The length, height, and quantity of a batch of wall panels required for production are represented by arrays e, f, and g, respectively. e = [e1 e2… e i …], f=[f1 f2 … f i …], g=[g1 g2 … g i …], where e i and f i represents the length and height of the i-th wall panel, and f i All satisfy f i ≤b-2d4,g i represents the number of the i-th type of wall panels;

[0008] S2. Calculation of mold combination

[0009] First, for alli <e i <For the wall panel of <b-2d4>, swap e and f in its dimensions. Then, combine the wall panels with the same length and height of the wall panel, and add up the quantities of the combined wall panels to form the final e, f, and g; i and f i For the wall panels with the same length and height of the wall panel, combine them, add up the quantities of the combined wall panels, and form the final e, f, and g;

[0010] Secondly, calculate all the matching combinations of the molds through a recursive function, that is, all possible matching combinations of different molds placed on the mold table that meet the requirements of the distance between the mold table and the molds, and represent them through the matrix Z, z mn represents the number of the m-th wall panel mold in the n-th combination;

[0011] S3. Establish an integer linear programming model

[0012] Each mold matching combination corresponds to a precast batch. The decision variable x is the number of times each column matching combination in Z is used, x = [x1 x2... x i …], where x i is the number of times the i-th cutting combination in Z is used; the constraint condition is that the quantity of each wall panel corresponding to x is equal to the production demand quantity of its corresponding wall panel, and an integer linear programming model is established;

[0013] S4. Solve the integer linear programming model

[0014] Use the genetic algorithm-based method to solve the integer linear programming model, and the optimal solution x of the integer linear programming can be obtained, that is, the optimization result of the wall panel precast mold combination.

[0015] Preferably, in the step S2, calculating all the matching combinations of the molds through a recursive function specifically includes the following steps:

[0016] S1. Calculate the initial combination matrix Z0 = [0 0... 0], and the length of the matrix is len(e), where len(e) is the number of elements in e;

[0017] S2. Establish a recursive function Z, and the input parameters of the function are a, d1, d2, d3, and e. The function calculation steps are as follows:

[0018] S21. Calculate (a - d1 - d3 + d2) / (e i + d2), and round down to get h = [h1 h2... h i …], where h i = [(a - d1 - d3 + d2) / (e i + d2)], and "[]" represents rounding down;

[0019] S22, determine whether len(e) is 1, if it is 1, then Z = [0 1…h1], output Z, if not 1, proceed to the next step;

[0020] S23, remove the first element from array e and recursively call function Z, Z t = Z(a d1 d2 d3 e);

[0021] S24, set a variable k, the initial value of k is 0, each loop k increases by 1, when k is greater than h1, exit the loop, in each loop, establish the matrix Z k , the matrix Z k Z t Add a row above, all elements in this row are k;

[0022] S25, combining matrices Z0, Z1, ... into Z;

[0023] S26, calculate the length L and the number of molds c of each column combination in Z, delete the column where L+d2(c-1)+d1+d3>a, and output Z;

[0024] S3. Finally, the combined matrix Z is obtained.

[0025] Preferably, in S3, the integer linear programming model is as follows:

[0026]

[0027] Preferably, in S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the accuracy of the variable value, and since all elements in x are non-negative integers, precision is 1; size_pop is the population size, which is 200, max_iter is the maximum number of iterations, which is 1200, prob_mut is the mutation probability, which is 0.02, and lb is the minimum value of each independent variable, which is 0;

[0028] The constraints are expressed as

[0029] The present invention includes at least the following beneficial effects: the present invention aims to minimize the mold platform batches, establishes a mold matching combination optimization model, can calculate the optimal mold combination production batch plan through mathematical programming methods, adopts a recursive method to achieve efficient exhaustive enumeration of mold combinations, and can adaptively adjust the direction of the wall panels to further save mold platform space, ultimately achieving improved production efficiency of prefabricated wall panels.

[0030] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the layout of the prefabricated wall panel production module in the present invention.

[0032] Figure 2 Schematic diagram of step S2 in the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0034] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0035] Those skilled in the art should understand that, in the disclosure of the present invention, the orientation or position relationship indicated by the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. are based on the orientation or position relationship shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the above terms should not be understood as limiting the present invention.

[0036] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0037] like Figure 1 As shown, a preferred embodiment of the present invention provides a method for optimizing a prefabricated mold for a prefabricated residential wall panel, comprising the following steps:

[0038] S1. Scene Description

[0039] Determine information on the production modules of prefabricated wall panels for prefabricated housing;

[0040] Among them, the precast wall panel production module includes a casting bed and molds. A plurality of the molds are arranged in a row at intervals, namely Mold 1, Mold 2, Mold 3... and are located on the casting bed in a straight line. The length of the casting bed is a, and the width is b. All the casting beds as a whole have a distance of not less than d1 from the edge of the mold on the left side, a distance of not less than d3 from the edge of the mold on the right side, distances of not less than d4 from the mold on the upper and lower sides, and a distance of not less than d2 between adjacent molds;

[0041] The wall panels are all rectangular. The lengths, heights, and quantities required for the production of a batch of wall panels are represented by arrays e, f, and g respectively. e = [e1 e2… e i …], f = [f1 f2 … f i …], g = [g1 g2 … g i …], where e i and f i represent the length and height of the i-th type of wall panel, and f i all satisfy f i ≤b - 2d4, and g i represents the quantity of the i-th type of wall panel;

[0042] S2. Calculation of mold matching combinations

[0043] First, for all wall panels that satisfy f i <e i <b - 2d4, swap the e i and f i in their dimensions. Then, merge the wall panels with the same length and height, and add up the quantities of the merged wall panels and put them together to form the final e, f, and g;

[0044] Secondly, calculate all the matching combinations of the molds through a recursive function, that is, all possible matching combinations of different molds placed on the casting bed and meeting the spacing requirements between the casting bed and the molds, and represent them through matrix Z, z mn represents the number of the m-th wall panel mold in the n-th combination;

[0045] S3. Establish an integer linear programming model

[0046] Each mold matching combination corresponds to a precast batch. The decision variable x is the number of times each column matching combination in Z is used. x = [x1 x2…x i …], where x i is the number of times the i-th column cutting combination in Z is used; the constraint condition is that the quantity of each wall panel corresponding to x is equal to the production demand quantity of its corresponding wall panel, and an integer linear programming model is established;

[0047] S4. Solve the integer linear programming model

[0048] By solving the integer linear programming model based on genetic algorithm, the optimal solution x of the integer linear programming can be obtained, that is, the optimization result of the wall panel prefabrication mold combination.

[0049] In another technical solution, in S2, all the matching combinations of the molds are calculated by a recursive function, such as Figure 2 As shown, the specific steps include:

[0050] S1. Calculate the initial combination matrix Z0 = [0 0…0], the matrix length is len(e), len(e) is the number of elements in e;

[0051] S2. Create a recursive function Z. The function input parameters are a, d1, d2, d3, and e. The function calculation steps are as follows:

[0052] S21, calculate (a-d1-d3+d2) / (e i +d2), and round down to get h = [h1 h2…h i …], where h i =[(a-d1-d3+d2) / (e i +d2)], “[]” means round down;

[0053] S22, determine whether len(e) is 1, if it is 1, then Z = [0 1…h1], output Z, if not 1, proceed to the next step;

[0054] S23, remove the first element from array e and recursively call function Z, Z t = Z(a d1 d2 d3 e);

[0055] S24, set a variable k, the initial value of k is 0, each loop k increases by 1, when k is greater than h1, exit the loop, in each loop, establish the matrix Z k , the matrix Z k Z t Add a row above, all elements in this row are k;

[0056] S25, combining matrices Z0, Z1, ... into Z;

[0057] S26, calculate the length L and the number of molds c of each column combination in Z, delete the column where L+d2(c-1)+d1+d3>a, and output Z;

[0058] S3. Finally, the combined matrix Z is obtained.

[0059] In another technical solution, in S3, the integer linear programming model is as follows:

[0060]

[0061]

[0062] Preferably, in the above S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the precision of the variable value. Since the elements in x are all non-negative integers, precision is taken as 1; size_pop is the population size, taken as 200, max_iter is the maximum number of iterations, taken as 1200, prob_mut is the mutation probability, taken as 0.02, lb is the minimum value of each independent variable, taken as 0;

[0063] The constraint condition is expressed as

[0064] Implementation case

[0065] 1) Case scenario

[0066] In a precast component factory of a prefabricated residential project, there is a batch of wall panels to be precast. The requirements of the wall panels are as follows: the length and height of wall panel 1 are 7660mm×3180mm, and the required quantity is 2 pieces; the length and height of wall panel 2 are 3360mm×3180mm, and the required quantity is 7 pieces; the length and height of wall panel 3 are 5520mm×3180mm, and the required quantity is 4 pieces. The length of the precast mold table is 12000mm, the width is 4000mm. The distance between the left side of the mold table and the mold is not less than 400mm, the distance between the right side of the mold table and the mold is not less than 300mm, the distances between the upper and lower sides of the mold table and the mold are not less than 300mm, and the distance between the molds is not less than 500mm.

[0067] According to the scenario, it can be known that a = 12000, b = 4000, d1 = 400, d2 = 500, d3 = 300, d4 = 300, e

[0068] = [7660 3360 5520], f = [3180 3180 3180], g = [2 7 4], where, f i all satisfy f i ≤ b - 2d4.

[0069] 2) Calculation of mold matching combination

[0070] First of all, wall panel 2 satisfies f i < e2 < b - 2d4, so e2 and f2 are swapped, and e, f, g are updated to e

[0071] = [7660 3180 5520], f = [3180 3360 3180], g = [2 7 4]. Calculate the mold matching combination according to the following steps:

[0072] S1, calculate (a-d1-d3+d2) / (e i + d2) and round down to get h = [1 3 1].

[0073] S2, len(e) = 3, remove the first element of array e, then e = [31805520], recursively call function Z, Z t =Z(ad1 d2 d3 e), the recursive calling steps are as follows:

[0074] S21, calculate (a-d1-d3+d2) / (e i + d2) and round down to get h = [3 1].

[0075] S22, len(e) = 2, remove the first element of array e, then e =

[5520] , recursively call function Z, Z t =Z(ad1d2d3e), the recursive calling steps are as follows:

[0076] S221, calculate (a-d1-d3+d2) / (e i + d2) and round down to get h = [1].

[0077] S222, len(e) = 1, then Z = [0 1], output Z = [0 1].

[0078] S23, h1=3, set a variable k, k takes the initial value of 0, each loop k increases by 1 until k=h1, the first loop k=0, establish the matrix Z0, The second cycle k = 1, build matrix Z1, The third cycle k = 2, build matrix Z2, The fourth cycle k = 3, build matrix Z3, Then exit the loop and merge Z0, Z1, Z2, and Z3 into

[0079] S24. Calculate the length L and the number of molds c of each column combination in Z, delete the columns where L+d2(c-1)+d1+d3>a, and obtain Output Z.

[0080] S3, h1 = 1, then two matrices are established, Merge Z0 and Z1 into

[0081] S4, delete the columns where L+d2(c-1)+d1+d3>a, and delete the columns with all zeros,

[0082] get Output Z.

[0083] 3) Establish an integer linear programming model

[0084] Then the decision variable is x = [x1 x2 … x6], and the integer linear programming model is as follows:

[0085]

[0086]

[0087] 4) Solve the integer linear programming model

[0088] Genetic algorithm is used to solve the integer linear programming model, and the genetic algorithm parameters are set as follows:

[0089] Table 1

[0090] Parameter name Configuration Values n_dim 6 size_pop 200 max_iter 1200 prob_mut 0.02 lb 0 precision (1,1,1,1,1,1)

[0091] The optimal solution is x = [0 0 4 0 1 2], and the mold optimization combination scheme is as follows:

[0092] Table 2

[0093]

[0094] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0095] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for optimizing a prefabricated mold for assembled residential wall panels, characterized in that: The following steps are involved: S1. Scene Description Determine information on the production modules of prefabricated wall panels for prefabricated housing; The prefabricated wall panel production module includes a mold platform and a mold. A plurality of the molds are arranged at intervals and are located on the mold platform in a straight line. The length of the mold platform is a and the width is b. All the mold platforms as a whole have a distance between the left side and the edge of the mold of no less than d1, a distance between the right side of the whole mold platform and the edge of the mold of no less than d3, a distance between the upper side and the lower side of the whole mold platform and the mold of no less than d4, and a distance between two adjacent molds of no less than d2. The wall panels are all rectangular. The length, height, and quantity of a batch of wall panels required for production are represented by arrays e, f, and g, respectively. e=[e1 e2 …e i …], f=[f1 f2 … f i …], g=[g1 g2 … g i …], where e i and f i represents the length and height of the i-th wall panel, and f i All satisfy f i ≤b-2d4,g i represents the number of the i-th type of wall panels; S2. Calculation of mold combination First, for all wall panels that satisfy f i <e i <b - 2d4, swap e i and f i in their dimensions. Then, merge the wall panels with the same length and height, add up the quantities of the merged wall panels, and form the final e, f, g; Secondly, all possible combinations of molds are calculated through recursive functions, that is, all possible combinations of different molds placed on the mold platform that meet the requirements of the distance between the mold platform and the mold, and are represented by matrix Z. z mn represents the number of m-th wall panel molds in the n-th combination; S3. Establishing integer linear programming model Each mold combination corresponds to a prefabrication batch, and the decision variable x is the number of times each combination in Z is used, x = [x1x2…x i …], where x i is the number of times the truncation combination of the i-th column in Z is used; the constraint condition is that the number of each wallboard corresponding to x is equal to the production demand quantity of the corresponding wallboard, and an integer linear programming model is established; S4. Solve integer linear programming models By solving the integer linear programming model based on genetic algorithm, the optimal solution x of the integer linear programming can be obtained, that is, the optimization result of the wall panel prefabrication mold combination.

2. The method for optimizing the prefabricated mold for assembled residential wall panels according to claim 1, characterized in that: In S2, all the matching combinations of the molds are calculated by a recursive function, which specifically includes the following steps: S1. Calculate the initial combination matrix Z0 = [0 0…0], the matrix length is len(e), len(e) is the number of elements in e; S2. Create a recursive function Z. The function input parameters are a, d1, d2, d3, and e. The function calculation steps are as follows: S21, calculate (a-d1-d3+d2) / (e i +d2), and round down to get h = [h1 h2…h i …], where h i =[(a-d1-d3+d2) / (e i +d2)], "[]" means round down; S22, determine whether len(e) is 1, if it is 1, then Z = [0 1…h1], output Z, if not 1, proceed to the next step; S23, remove the first element from array e and recursively call function Z, Z t = Z(a d1 d2 d3 e); S24, set a variable k, the initial value of k is 0, each loop k increases by 1, when k is greater than h1, exit the loop, in each loop, establish the matrix Z k , the matrix Z k Z t Add a row above, all elements in this row are k; S25, combining matrices Z0, Z1, ... into Z; S26, calculate the length L and the number of molds c of each column combination in Z, delete the column where L+d2(c-1)+d1+d3>a, and output Z; S3. Finally, the combined matrix Z is obtained.

3. The method for optimizing the prefabricated mold for assembled residential wall panels according to claim 1, characterized in that: In S3, the integer linear programming model is as follows:

4. The method for optimizing the prefabricated mold for assembled residential wall panels according to claim 1, characterized in that: In S3 and S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the accuracy of the variable value, and since all elements in x are non-negative integers, precision is 1; size_pop is the population size, which is 200, max_iter is the maximum number of iterations, which is 1200, prob_mut is the mutation probability, which is 0.02, and lb is the minimum value of each independent variable, which is 0; the constraint condition is expressed as

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